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Mcp Agent Trace Inspector

dbsectrainer/mcp-agent-trace-inspector
authSTDIOregistry active
Summary

This is local-first observability for MCP tool chains. Every tool call, token count, and latency measurement lands in a local SQLite database instead of a cloud service. You get 13 tools covering the full lifecycle: trace_start and trace_step to capture workflow execution, export_dashboard to generate a self-contained HTML waterfall view, compare_traces to diff two runs side by side, and configure_alerts for Slack or webhook notifications. Token cost estimates run offline using tiktoken and a configurable pricing table. Adds under 5ms overhead per step. Reach for this when your traces contain sensitive data that can't leave your machine, or when you need MCP-native instrumentation without LangSmith's API keys and account setup.

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MCP Agent Trace Inspector

npm mcp-agent-trace-inspector package

Local-first, MCP-native observability for agent workflows. Every tool call, prompt transformation, latency, and token count is recorded in a local SQLite database — no cloud account, no API key, no traces leaving your machine. Built specifically for MCP rather than bolted onto a generic LLM proxy.

Tool reference | Configuration | Contributing | Troubleshooting | Design principles

Key features

  • Tool call tracing: Captures inputs, outputs, latency, and token usage for every step in a workflow.
  • Persistent storage: Traces survive session restarts; stored locally in SQLite with no external dependencies.
  • HTML dashboard: Generates a self-contained single-file dashboard with an interactive step timeline.
  • Token cost estimation: Calculates USD cost per trace using a configurable model pricing table — no API calls required.
  • Trace comparison: Diff two traces side by side to measure the impact of prompt or tool changes.
  • Low overhead: Adds less than 5ms per step; never becomes the bottleneck.

Why this over LangSmith / AgentOps?

mcp-agent-trace-inspectorLangSmith / AgentOps
Data locationLocal SQLite — never leaves your machineCloud-hosted; traces sent to external servers
Setupnpx one-liner, zero configAccount signup, API key, SDK instrumentation
MCP-awareNative — records tool calls as first-class stepsGeneric LLM proxy; MCP structure is opaque
Run diffsBuilt-in compare_traces diffSeparate paid feature or manual export
Cost estimationOffline tiktoken + configurable pricing tableRequires live API traffic through their proxy
Overhead<5ms per stepNetwork round-trip per event

If your traces contain sensitive tool outputs, proprietary prompts, or data that must stay on-device, this is the right tool. If you need cross-team trace sharing or a managed SaaS, use LangSmith.

Disclaimers

mcp-agent-trace-inspector stores tool call inputs and outputs locally in a SQLite database. Traces may contain sensitive information passed to or returned from your tools. Review trace contents before sharing dashboard exports. Traces are not automatically transmitted; optional alert webhooks are available.

Requirements

  • Node.js v22.5.0 or newer.
  • npm.

Getting started

Add the following config to your MCP client:

{
  "mcpServers": {
    "trace-inspector": {
      "command": "npx",
      "args": ["-y", "mcp-agent-trace-inspector@latest"]
    }
  }
}

To set a custom storage path:

{
  "mcpServers": {
    "trace-inspector": {
      "command": "npx",
      "args": [
        "-y",
        "mcp-agent-trace-inspector@latest",
        "--db=~/traces/my-project.db"
      ]
    }
  }
}

MCP Client configuration

Amp · Claude Code · Cline · Cursor · VS Code · Windsurf · Zed

Your first prompt

Enter the following in your MCP client to verify everything is working:

Start a trace called "test-run", then list the files in the current directory, then end the trace and show me the summary.

Your client should return a summary showing step count, total tokens, and latency.

Tools

Trace lifecycle (3 tools)

  • trace_start — begin a new trace; returns a trace_id for subsequent calls
  • trace_step — record one tool call step (inputs, outputs, optional token count and latency)
  • trace_end — mark a trace as completed

Inspection (4 tools)

  • list_traces — list stored traces with names, statuses, and timestamps
  • get_trace_summary — token totals, step count, latency, and cost estimate for a trace
  • compare_traces — diff two traces side by side (step counts, tokens, latency)
  • extract_reasoning_chain — extract only reasoning/thinking steps from a trace

Export (3 tools)

  • export_dashboard — generate a self-contained single-file HTML dashboard with latency waterfall
  • export_otel — export one or all traces in OpenTelemetry OTLP JSON span format
  • export_compliance_log — export the compliance audit log as JSON or CSV, with optional date range filtering

Operations (3 tools)

  • configure_alerts — configure alert rules on latency, error rate, or cost; fire to Slack or generic webhooks
  • set_retention_policy — set how many days to keep traces (in-memory; must be called before apply_retention)
  • apply_retention — archive traces older than the configured threshold; delete traces past 2x the threshold

Configuration

--db / --db-path

Path to the SQLite database file used to store traces.

Type: string Default: ~/.mcp/traces.db

--retention-days

Automatically delete traces older than N days. Set to 0 to disable.

Type: number Default: 0

--pricing-table

Path to a JSON file containing custom model pricing ($/1K tokens). Overrides the built-in table.

Type: string

--no-token-count

Disable tiktoken-based token counting. Traces will omit token usage metrics.

Type: boolean Default: false

Pass flags via the args property in your JSON config:

{
  "mcpServers": {
    "trace-inspector": {
      "command": "npx",
      "args": ["-y", "mcp-agent-trace-inspector@latest", "--retention-days=30"]
    }
  }
}

Design principles

  • Append-only traces: Steps are immutable once recorded. Trust requires integrity.
  • Local-first: All core functionality works without a network connection.
  • Portable dashboards: HTML exports are always single-file; no server required to view them.

Verification

Before publishing a new version, verify the server with MCP Inspector to confirm all tools are exposed correctly and the protocol handshake succeeds.

Interactive UI (opens browser):

npm run build && npm run inspect

CLI mode (scripted / CI-friendly):

# List all tools
npx @modelcontextprotocol/inspector --cli node dist/index.js --method tools/list

# List resources and prompts
npx @modelcontextprotocol/inspector --cli node dist/index.js --method resources/list
npx @modelcontextprotocol/inspector --cli node dist/index.js --method prompts/list

# Call a tool (example — replace with a relevant read-only tool for this plugin)
npx @modelcontextprotocol/inspector --cli node dist/index.js \
  --method tools/call --tool-name list_traces

# Call a tool with arguments
npx @modelcontextprotocol/inspector --cli node dist/index.js \
  --method tools/call --tool-name list_traces --tool-arg key=value

Run before publishing to catch regressions in tool registration and runtime startup.

Contributing

See CONTRIBUTING.md for full contribution guidelines.

npm install && npm test

MCP Registry & Marketplace

This plugin is available on:

  • MCP Registry
  • MCP Market

Search for mcp-agent-trace-inspector.

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Configuration

YOUR_API_KEY*secret

Your API key for the service

Categories
AI & LLM ToolsMonitoring & Observability
Registryactive
Packagemcp-agent-trace-inspector
TransportSTDIO
AuthRequired
UpdatedMar 23, 2026
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